GFlowNets solve the variational inference problem of approximating a target distribution over \(\mathcal {X}\)  with the distribution induced by the sampling policy, and they are trained by algorithms reminiscent of reinforcement learning (although GFlowNets model the diversity present in the reward distribution, rather than maximizing reward by seeking its mode). In most past works [1]}, [2]}, [3]}, [4]}, GFlowNets are trained by exploratory sampling from the policy and receive their training signal from the reward of the sampled object. The flow matching (FM) and detailed balance (DB) learning objectives for GFlowNets proposed in [1]}, [6]} resemble temporal difference learning [7]}.
